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An End-to-End Campus-Scale High Performance Cyberinfrastructure for Data-Inte...Larry Smarr
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The Annual Robert Stewart Distinguished Lecture
Iowa State University
Title: An End-to-End Campus-Scale High Performance Cyberinfrastructure for Data-Intensive Research
Ames, IA
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Outstanding advancements in imaging technology have made cryogenic electron microscopy a powerful technique for the nanocharacterization of biological macromolecular complexes, reaching atomic levels of resolution and being applicable to a wider set of samples than the other competing technologies. The real breakthrough in the development of cryo-EM has happened less than a decade ago, with the introduction of direct detection devices. These cameras allow unprecedented speed and resolution, and Lawrence Berkeley National Lab is developing a new detector, the 4D cam- era, that can operate at 87000 frames per second, revealing exclusive temporal dynamics of the investigated processes.
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Volume ray casting algorithms benefit greatly with recent increase of GPU capabilities and power. In this paper,
we present a novel memory efficient ray casting algorithm for unstructured grids completely implemented on GPU
using a recent off-the-shelf nVidia graphics card. Our approach is built upon a recent CPU ray casting algorithm,
called VF-Ray, that considerably reduces the memory footprint while keeping good performance. In addition to
the implementation of VF-Ray in the graphics hardware, we also propose a restructuring in its data structures. As
a result, our algorithm is much faster than the original software version, while using significantly less memory, it
needed only one-half of its previous memory usage. Comparing our GPU implementation to other hardware-based
ray casting algorithms, our approach used between three to ten times less memory. These results made it possible
for our GPU implementation to handle larger datasets on GPU than previous approaches.
Lecture by Xavier Giro-i-Nieto (UPC) at the Master in Computer Vision Barcelona (March 30, 2016).
http://pagines.uab.cat/mcv/
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Please submit your feedback as comments on the GDrive source slides:
https://docs.google.com/presentation/d/1ms9Fczkep__9pMCjxtVr41OINMklcHWc74kwANj7KKI/edit?usp=sharing
Three dimensional particle image velocimetrypawankumar9275
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Volume ray casting algorithms benefit greatly with recent increase of GPU capabilities and power. In this paper,
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using a recent off-the-shelf nVidia graphics card. Our approach is built upon a recent CPU ray casting algorithm,
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a result, our algorithm is much faster than the original software version, while using significantly less memory, it
needed only one-half of its previous memory usage. Comparing our GPU implementation to other hardware-based
ray casting algorithms, our approach used between three to ten times less memory. These results made it possible
for our GPU implementation to handle larger datasets on GPU than previous approaches.
Lecture by Xavier Giro-i-Nieto (UPC) at the Master in Computer Vision Barcelona (March 30, 2016).
http://pagines.uab.cat/mcv/
This lecture provides an overview of computer vision analysis of images at a global scale using deep learning techniques. The session is structure in two blocks: a first one addressing end to end learning, and a second one focusing on applications that use off-the-shelf features.
Please submit your feedback as comments on the GDrive source slides:
https://docs.google.com/presentation/d/1ms9Fczkep__9pMCjxtVr41OINMklcHWc74kwANj7KKI/edit?usp=sharing
Three dimensional particle image velocimetrypawankumar9275
Three-dimensional particle image velocimetry for the flows near the wall. A brief introduction to measurement methods in fluid fields in general and imaging methods in particular. A general overview of famous three-dimensional particle imaging methods and detailed description of image velocimetry for the near wall flows
Session 10 in module 3 from the Master in Computer Vision by UPC, UAB, UOC & UPF.
This lecture provides an overview of state of the art applications of convolutional neural networks to the problems in video processing: semantic recognition, optical flow estimation and object tracking.
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Computational Discovery of Two-Dimensional Materials, Evaluation of Force-Fie...KAMAL CHOUDHARY
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A Network Based Kernel Density Estimator Applied to Barcelona Economic Activities Timothée Produit, Nicolas Lachance-Bernard, Stéphane Joost
1. A Network Based Kernel Density Estimator
Applied to Barcelona Economic Activities
Ecole Polytechnique Fédérale de Lausanne, Switzerland
LaSIG
Timothée Produit, Nicolas Lachance-Bernard, Stéphane Joost
University of Strathclyde, Glasgow, United Kingdom
Urban Design Studies Unit
Sergio Porta, Emanuele Strano
Fukuoka, ICCSA, March 2010
2. Plan
• Goals
• Theory
• Methodology and Algorithms
• Barcelona case study
• Conclusion
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
3. Goals
• Goals
– Create a network oriented density indicator to study cities design
– Compare NetKDE indicator with KDE indicator
– Complete a proof-of-concept
• Apply NetKDE to economic activities (points)
• Apply NetKDE to network edges weighted by centrality indexes (polylines)
• Technologies
– Python Scripts, PostGIS Database, ArcGIS
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
4. Theory
• Kernel Density Estimator (KDE):
– Operates in Euclidean space
– Weights events according to their radial distances from grid centroid
• Network based KDE (NetKDE):
– Operates in a Network Constrained space
– Weights events according to the distance measured along this
network
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
5. KDE vs NetKDE
KDE: For each raster cell, events inside a NetKDE: For each raster cell, projected events
radial bandwith contribute to density along a network bandwith contribute to density
evaluation. evaluation.
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
6. KDE vs NetKDE
KDE: The Kernel function weights events NetKDE: The Kernel function weights events
according to their radial distance according to the distance measured along the
network
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
8. Input data
» Activities are stored in a shape file,
» Network is stored in a shape file,
» Creation of a raster grid covering the extent of the
network
ArcGIS
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
9. Conversion
» Files are exported into a PostGIS
database
» The raster grid is converted into points
PostGIS
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
10. Activities projection
Projection
» Activities are projected on the nearest
edge
PostGIS
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
11. Activities projection
Projection
PostGIS
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
12. Calculation
For each cell of the grid (represented by its
centroid), the script imports the surrounding
network and projected activities
PostGIS Python Script
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
13. Calculation
The Script compute a Shortest Path Tree for
the current raster cell, the
NetKDE of point and the NetKDE of edges.
PostGIS Python
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
14. Barcelona Case Study
• Material
– Network: 11,000 edges
– Activities: 166,000 economic activities listed by the
Agencia de Ecologia Urbana in 2002
Network and
activities
• Computation
– 926,000 raster cells, 10 meter resolution
– 400 meter bandwith
– 33 hours (Intel(R) Core(TM)2 Quad CPU, Q950 @ 3.00GHz, 2.99Ghz, 7.83 GB of RAM)
Global
• Zoom in on the center of Barcelona
betweenness
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
15. Kernel Density of activities
Several activities
can be located at
the same place
Bandwith = 400m
Computed with
ArcGIS
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
16. Linear Density
Linear density of
activities =
Nbr. Act./ Length of
SPT
Same scale
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
17. NetKDE of activities
Kernel formula
• Activities:
applied to activities
projected on the
KDE of
network,
activities
Smaller patterns
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
18. KDE of global betweenness
Global betweenness
is an indicator
characterizing the
centrality of an
edge.
Values of edges are
generalized to the
entire space.
Computed with
ArcGIS.
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
19. NetKDE of global betweenness
For NetKDE of
edges,
the inputs are the
middle of edges and
global
betweenness.
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010
20. Conclusion
• This work proposes an innovative density indicator based
on a road network, to better fit the urban constraints on
human mobility.
• The processing using a PostGIS database is stable and
fast.
• Here are presented the first evaluation of the results
• Current researches are related to:
– Proofing NetKDE versus KDE (sensibility and geostatistical analysis)
– Correlation analysis between Activitities and NetKDE centrality
indicators
– Research on other cities : Barcelona, Glasgow, Geneva, Bologna,
Roma
EPFL – University of Strathclyde ICCSA, Geo-An-Mod 2010